Environmental microbiome diversity and stability is a barrier to antimicrobial resistance gene accumulation.

When antimicrobial resistant bacteria (ARB) and genes (ARGs) reach novel habitats, they can become part of the habitat's microbiome in the long term if they are able to overcome the habitat's biotic resilience towards immigration. This process should become more difficult with increasing biodiversity, as exploitable niches in a given habitat are reduced for immigrants when more diverse competitors are present. Consequently, microbial diversity could provide a natural barrier towards antimicrobial resistance by reducing the persistence time of immigrating ARB and ARG. To test this hypothesis, a pan-European sampling campaign was performed for structured forest soil and dynamic riverbed environments of low anthropogenic impact. In soils, higher diversity, evenness and richness were significantly negatively correlated with relative abundance of >85% of ARGs. Furthermore, the number of detected ARGs per sample were inversely correlated with diversity. However, no such effects were present in the more dynamic riverbeds. Hence, microbiome diversity can serve as a barrier towards antimicrobial resistance dissemination in stationary, structured environments, where long-term, diversity-based resilience against immigration can evolve.

Reduced selection for antibiotic resistance in community context is maintained despite pressure by additional antibiotics.

Selection for antibiotic resistance at very low antibiotic concentrations has been demonstrated for individual antibiotics in single species experiments. Furthermore, selection in these focal strains is reduced when taking place in complex microbial community context. However, in the environment, bacteria are rarely exposed to single, but rather complex mixtures of selective agents. Here, we explored how the presence of a second selective agent affects selection dynamics between isogenic pairs of focal E. coli strains, differing exclusively in a single resistance determinant, in the absence and presence of a model wastewater community across a gradient of antibiotics. An additional antibiotic that exclusively affects the model wastewater community, but to which the focal strains are resistant to, was chosen as the second selective agent. This allowed exploring how inhibition alters the community's ability to reduce selection. In the presence of the community, the selection coefficient at specific antibiotic concentrations was consistently decreased compared to the absence of the community. While pressure through the second antibiotic significantly decreased the activity and diversity of the community, its ability to reduce selection was consistently maintained at levels comparable to those recorded in absence of the second antibiotic. This indicates that the observed effects of community context on selection dynamics are rather based on competitive or protective effects between the focal strains and a small proportion of bacteria within the community, than on general competition for nutrients. These findings have implications for our understanding of the evolution and selection for multi-drug resistant strains.

Quantification of the mobility potential of antibiotic resistance genes through multiplexed ddPCR linkage analysis.

There is a clear need for global monitoring initiatives to evaluate the risks of antibiotic resistance genes (ARGs) towards human health. Therefore, not only ARG abundances within a given environment, but also their potential mobility, hence their ability to spread to human pathogenic bacteria needs to be quantified. We developed a novel, sequencing-independent method for assessing the linkage of an ARG to a mobile genetic element by statistical analysis of multiplexed droplet digital PCR (ddPCR) carried out on environmental DNA sheared into defined, short fragments. This allows quantifying the physical linkage between specific ARGs and mobile genetic elements, here demonstrated for the sulfonamide ARG sul1 and the Class 1 integron integrase gene intI1. The method's efficiency is demonstrated using mixtures of model DNA fragments with either linked and unlinked target genes: Linkage of the two target genes can be accurately quantified based on high correlation coefficients between observed and expected values (R2) as well as low mean absolute errors (MAE) for both target genes, sul1 (R2 = 0.9997, MAE = 0.71%, n = 24) and intI1 (R2 = 0.9991, MAE = 1.14%, n = 24). Furthermore, we demonstrate that adjusting the fragmentation length of DNA during shearing allows controlling rates of false positives and false negative detection of linkage. The presented method allows rapidly obtaining reliable results in a labor- and cost-efficient manner.